
import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)






def   lowerCamelCase__							(							__lowerCAmelCase    :    str							):




			"""simple docstring"""


			if hor == 128:
						lowerCAmelCase_         =      ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						lowerCAmelCase_         =      (32, 128, 256)
						lowerCAmelCase_         =      ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						lowerCAmelCase_         =      ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						lowerCAmelCase_         =      (32, 64, 128, 256)
						lowerCAmelCase_         =      ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			lowerCAmelCase_         =      torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""							)
			lowerCAmelCase_         =      model.state_dict()
			lowerCAmelCase_         =      {
			    "down_block_types": down_block_types,
			    "block_out_channels": block_out_channels,
			    "up_block_types": up_block_types,
			    "layers_per_block": 1,
			    "use_timestep_embedding": True,
			    "out_block_type": "OutConv1DBlock",
			    "norm_num_groups": 8,
			    "downsample_each_block": False,
			    "in_channels": 14,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "sample_size": 65536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			lowerCAmelCase_         =      UNetaDModel(**UpperCamelCase__							)
			print(F"""length of state dict: {len(state_dict.keys()							)}"""							)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()							)}"""							)
			lowerCAmelCase_         =      dict(zip(model.state_dict().keys()       ,				hf_value_function.state_dict().keys()							)							)
			for k, v in mapping.items():
						lowerCAmelCase_         =      state_dict.pop(UpperCamelCase__							)
			hf_value_function.load_state_dict(UpperCamelCase__							)

			torch.save(hf_value_function.state_dict()       ,				F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""							)
			with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""       ,				"w"							) as f:
						json.dump(UpperCamelCase__       ,				UpperCamelCase__							)






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      {
			    "in_channels": 14,
			    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
			    "up_block_types": (),
			    "out_block_type": "ValueFunction",
			    "mid_block_type": "ValueFunctionMidBlock1D",
			    "block_out_channels": (32, 64, 128, 256),
			    "layers_per_block": 1,
			    "downsample_each_block": True,
			    "sample_size": 65536,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "use_timestep_embedding": True,
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "norm_num_groups": 8,
			    "act_fn": "mish",
			}

			lowerCAmelCase_         =      torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
			lowerCAmelCase_         =      model
			lowerCAmelCase_         =      UNetaDModel(**UpperCamelCase__							)
			print(F"""length of state dict: {len(state_dict.keys()							)}"""							)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()							)}"""							)

			lowerCAmelCase_         =      dict(zip(state_dict.keys()       ,				hf_value_function.state_dict().keys()							)							)
			for k, v in mapping.items():
						lowerCAmelCase_         =      state_dict.pop(UpperCamelCase__							)

			hf_value_function.load_state_dict(UpperCamelCase__							)

			torch.save(hf_value_function.state_dict()       ,				"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"							)
			with open("hub/hopper-medium-v2/value_function/config.json"       ,				"w"							) as f:
						json.dump(UpperCamelCase__       ,				UpperCamelCase__							)


if __name__ == "__main__":
			unet(32)
			# unet(128)
			value_function()




'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
